Target Type Tracking with PCR5 and Dempster's rules: A Comparative Analysis

TL;DR

This paper compares Dempster's rule and PCR5 for target type estimation, showing PCR5's superior ability to quickly and accurately track target type changes in sequential data fusion scenarios.

Contribution

It introduces a new comparative analysis of Dempster's rule and PCR5, demonstrating PCR5's effectiveness in real-time target type tracking and change detection.

Findings

PCR5 reduces latency in target type decision-making.

Dempster's rule fails to detect short target type switches.

PCR5 outperforms Dempster's rule in dynamic target tracking scenarios.

Abstract

In this paper we consider and analyze the behavior of two combinational rules for temporal (sequential) attribute data fusion for target type estimation. Our comparative analysis is based on Dempster's fusion rule proposed in Dempster-Shafer Theory (DST) and on the Proportional Conflict Redistribution rule no. 5 (PCR5) recently proposed in Dezert-Smarandache Theory (DSmT). We show through very simple scenario and Monte-Carlo simulation, how PCR5 allows a very efficient Target Type Tracking and reduces drastically the latency delay for correct Target Type decision with respect to Demspter's rule. For cases presenting some short Target Type switches, Demspter's rule is proved to be unable to detect the switches and thus to track correctly the Target Type changes. The approach proposed here is totally new, efficient and promising to be incorporated in real-time Generalized Data Association - Multi Target Tracking systems (GDA-MTT) and provides an important result on the behavior of PCR5 with respect to Dempster's rule. The MatLab source code is provided in